Load the dependencies

Loadind the data

Q1: Price range year round

Convert ['date'] column from object type to datetime type

Remove the "$" , ".", "," signS from the ['price'] column and convert it to float

We can see that the monthly average price peaks during the month of JULY and is usually high between MAY and SEPTEMBER

Q2: Busy and expensive Neighbouhoods

Drop those 7 rows with missing zipcode values for our calculation

Map is tilted 90 degrees to the left

Top 3 zipcodes with most Airbnb listings are

They combine to form more than 30% of the AIRBNB listings in Seattle

We can ignore the "Other neighborhoods" in this analysis

Convert price to float format

Replace "99\n98122" to "98122" as it's a typo

Neighbourhoods with the most expensive listing prices in Seattle (higher than mean price $ 127) are shown above

Neighbourhoods with affordable prices (lower than mean price $ 127) are shown above

Q3: Most important features affecting the price

Considering few important features

A lot of quantitative features impact the price like the number of beds, bedrooms, bathrooms and accommodates

Due to restricts on project complexity we focused only on continous values

Occupancy

We can observe that occupation rate is low mostly during March, November & December